# -*- coding: utf-8 -*-
# code is from https://github.com/speedinghzl/Pytorch-Deeplab/blob/master/deeplab/model.py
import time

import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch
import numpy as np
from torch.autograd import Variable
import torch.nn.functional as F

from semseg.modelloader.utils import ASPP_Classifier_Module
from semseg.loss import cross_entropy2d

# affine_par = True


def outS(i):
    i = int(i)
    i = (i+1)/2
    i = int(np.ceil((i+1)/2.0))
    i = (i+1)/2
    return i

def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False) # change
        self.bn1 = nn.BatchNorm2d(planes)
        for i in self.bn1.parameters():
            i.requires_grad = False

        padding = dilation
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, # change
                               padding=padding, bias=False, dilation = dilation)
        self.bn2 = nn.BatchNorm2d(planes)
        for i in self.bn2.parameters():
            i.requires_grad = False
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        for i in self.bn3.parameters():
            i.requires_grad = False
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride


    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out

# class Classifier_Module(nn.Module):
#
#     def __init__(self, dilation_series, padding_series, num_classes):
#         super(Classifier_Module, self).__init__()
#         self.conv2d_list = nn.ModuleList()
#         for dilation, padding in zip(dilation_series, padding_series):
#             self.conv2d_list.append(nn.Conv2d(2048, num_classes, kernel_size=3, stride=1, padding=padding, dilation=dilation, bias = True))
#
#         for m in self.conv2d_list:
#             m.weight.data.normal_(0, 0.01)
#
#     def forward(self, x):
#         out = self.conv2d_list[0](x)
#         for i in range(len(self.conv2d_list)-1):
#             out += self.conv2d_list[i+1](x)
#         return out

# class Residual_Covolution(nn.Module):
#     def __init__(self, icol, ocol, num_classes):
#         super(Residual_Covolution, self).__init__()
#         self.conv1 = nn.Conv2d(icol, ocol, kernel_size=3, stride=1, padding=12, dilation=12, bias=True)
#         self.conv2 = nn.Conv2d(ocol, num_classes, kernel_size=3, stride=1, padding=12, dilation=12, bias=True)
#         self.conv3 = nn.Conv2d(num_classes, ocol, kernel_size=1, stride=1, padding=0, dilation=1, bias=True)
#         self.conv4 = nn.Conv2d(ocol, icol, kernel_size=1, stride=1, padding=0, dilation=1, bias=True)
#         self.relu = nn.ReLU(inplace=True)
#
#     def forward(self, x):
#         dow1 = self.conv1(x)
#         dow1 = self.relu(dow1)
#         seg = self.conv2(dow1)
#         inc1 = self.conv3(seg)
#         add1 = dow1 + self.relu(inc1)
#         inc2 = self.conv4(add1)
#         out = x + self.relu(inc2)
#         return out, seg

# class Residual_Refinement_Module(nn.Module):
#
#     def __init__(self, num_classes):
#         super(Residual_Refinement_Module, self).__init__()
#         self.RC1 = Residual_Covolution(2048, 512, num_classes)
#         self.RC2 = Residual_Covolution(2048, 512, num_classes)
#
#     def forward(self, x):
#         x, seg1 = self.RC1(x)
#         _, seg2 = self.RC2(x)
#         return [seg1, seg1+seg2]

# class ResNet_Refine(nn.Module):
#     def __init__(self, block, layers, n_classes):
#         self.inplanes = 64
#         super(ResNet_Refine, self).__init__()
#         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
#                                bias=False)
#         self.bn1 = nn.BatchNorm2d(64, affine = affine_par)
#         for i in self.bn1.parameters():
#             i.requires_grad = False
#         self.relu = nn.ReLU(inplace=True)
#         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True) # change
#         self.layer1 = self._make_layer(block, 64, layers[0])
#         self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
#         self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2)
#         self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4)
#         self.layer5 = Residual_Refinement_Module(n_classes)
#
#         for m in self.modules():
#             if isinstance(m, nn.Conv2d):
#                 n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
#                 m.weight.data.normal_(0, 0.01)
#             elif isinstance(m, nn.BatchNorm2d):
#                 m.weight.data.fill_(1)
#                 m.bias.data.zero_()
#         #        for i in m.parameters():
#         #            i.requires_grad = False
#
#     def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
#         downsample = None
#         if stride != 1 or self.inplanes != planes * block.expansion or dilation == 2 or dilation == 4:
#             downsample = nn.Sequential(
#                 nn.Conv2d(self.inplanes, planes * block.expansion,
#                           kernel_size=1, stride=stride, bias=False),
#                 nn.BatchNorm2d(planes * block.expansion,affine = affine_par))
#         for i in downsample._modules['1'].parameters():
#             i.requires_grad = False
#         layers = []
#         layers.append(block(self.inplanes, planes, stride,dilation=dilation, downsample=downsample))
#         self.inplanes = planes * block.expansion
#         for i in range(1, blocks):
#             layers.append(block(self.inplanes, planes, dilation=dilation))
#
#         return nn.Sequential(*layers)
#
#     def forward(self, x):
#         x = self.conv1(x)
#         x = self.bn1(x)
#         x = self.relu(x)
#         x = self.maxpool(x)
#         x = self.layer1(x)
#         x = self.layer2(x)
#         x = self.layer3(x)
#         x = self.layer4(x)
#         x = self.layer5(x)
#
#         return x

class ResNet(nn.Module):
    def __init__(self, block, layers, n_classes):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        for i in self.bn1.parameters():
            i.requires_grad = False
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True) # change
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4)
        # self.layer5 = self._make_pred_layer(Classifier_Module, [6,12,18,24], [6,12,18,24], n_classes)
        self.layer5 = self._make_pred_layer(ASPP_Classifier_Module, [6,12,18,24], [6,12,18,24], n_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, 0.01)
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
        #        for i in m.parameters():
        #            i.requires_grad = False

    def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion or dilation == 2 or dilation == 4:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion))

        for i in downsample._modules['1'].parameters():
            i.requires_grad = False

        layers = []
        layers.append(block(self.inplanes, planes, stride,dilation=dilation, downsample=downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, dilation=dilation))

        return nn.Sequential(*layers)

    def _make_pred_layer(self,block, dilation_series, padding_series,num_classes):
        return block(dilation_series,padding_series,num_classes)

    def forward(self, x):
        x_size = x.size()[2:]

        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.layer5(x)

        x = F.upsample_bilinear(x, x_size)
        return x

# class MS_Deeplab(nn.Module):
#     def __init__(self,block,num_classes):
#         super(MS_Deeplab,self).__init__()
#         self.Scale = ResNet(block,[3, 4, 23, 3],num_classes)   #changed to fix #4
#
#     def forward(self,x):
#         output = self.Scale(x) # for original scale
#         output_size = output.size()[2]
#         input_size = x.size()[2]
#
#         self.interp1 = nn.Upsample(size=(int(input_size*0.75)+1, int(input_size*0.75)+1), mode='bilinear')
#         self.interp2 = nn.Upsample(size=(int(input_size*0.5)+1, int(input_size*0.5)+1), mode='bilinear')
#         self.interp3 = nn.Upsample(size=(output_size, output_size), mode='bilinear')
#
#         x75 = self.interp1(x)
#         output75 = self.interp3(self.Scale(x75)) # for 0.75x scale
#
#         x5 = self.interp2(x)
#         output5 = self.interp3(self.Scale(x5))	# for 0.5x scale
#
#         out_max = torch.max(torch.max(output, output75), output5)
#         return [output, output75, output5, out_max]

# def Res_Ms_Deeplab(num_classes=21):
#     model = MS_Deeplab(Bottleneck, num_classes)
#     return model

# def Res_Deeplab(n_classes=21):
#     # if is_refine:
#     #     model = ResNet_Refine(Bottleneck,[3, 4, 23, 3], n_classes)
#     # else:
#     #     model = ResNet(Bottleneck,[3, 4, 23, 3], n_classes)
#     model = ResNet(Bottleneck,[3, 4, 23, 3], n_classes)
#     return model

# def Res_Deeplab_18(n_classes=21, is_refine=False):
#     if is_refine:
#         model = ResNet_Refine(BasicBlock,[2, 2, 2, 2], n_classes)
#     else:
#         model = ResNet(BasicBlock,[2, 2, 2, 2], n_classes)
#     return model

# def Res_Deeplab_34(n_classes=21, is_refine=False):
#     if is_refine:
#         model = ResNet_Refine(BasicBlock,[3, 4, 6, 3], n_classes)
#     else:
#         model = ResNet(BasicBlock,[3, 4, 6, 3], n_classes)
#     return model

def Res_Deeplab_50(n_classes=21, pretrained=False):
    # if is_refine:
    #     pass
    #     # model = ResNet_Refine(Bottleneck, [3, 4, 6, 3], n_classes)
    # else:
    #     model = ResNet(Bottleneck, [3, 4, 6, 3], n_classes)
    model = ResNet(Bottleneck, [3, 4, 6, 3], n_classes)
    return model

def Res_Deeplab_101(n_classes=21, pretrained=False):
    # if is_refine:
    #     pass
    #     # model = ResNet_Refine(Bottleneck, [3, 4, 23, 3], n_classes)
    # else:
    #     model = ResNet(Bottleneck, [3, 4, 23, 3], n_classes)
    model = ResNet(Bottleneck, [3, 4, 23, 3], n_classes)
    return model

if __name__ == '__main__':
    batch_size = 1
    n_classes = 21
    model = Res_Deeplab_50(n_classes=n_classes)
    x = Variable(torch.randn(1, 3, 360, 480))
    y = Variable(torch.LongTensor(np.ones((1, 360, 480), dtype=np.int)))
    # print(x.shape)
    start = time.time()
    pred = model(x)
    end = time.time()
    print(end-start)
    print(pred.shape)
    print('pred.type:', pred.type)
    loss = cross_entropy2d(pred, y)
    # print(loss)